Proteomic Expression Data Research Articles

Proteomics of Crystal-Cell Interactions: A Model for Kidney Stone Research.

Nephrolithiasis/urolithiasis (i.e., kidney stone disease) remains a global public health problem with increasing incidence/prevalence. The most common chemical composition of kidney stones is calcium oxalate that initiates stone formation by crystallization, crystal growth, crystal aggregation, crystal–cell adhesion, and crystal invasion through extracellular matrix in renal interstitium. Among these processes, crystal–cell interactions (defined as “the phenomena in which the cell is altered by any means of effects from the crystal that adheres onto cellular surface or is internalized into the cell, accompanying with changes of the crystal, e.g., growth, adhesive capability, degradation, etc., induced by the cell”) are very important for crystal retention in the kidney. During the past 12 years, proteomics has been extensively applied to kidney stone research aiming for better understanding of the pathogenic mechanisms of kidney stone formation. This article provides an overview of the current knowledge in this field and summarizes the data obtained from all the studies that applied proteomics to the investigations of crystal–cell interactions that subsequently led to functional studies to address the significant impact or functional roles of the expression proteomics data in the pathogenesis of kidney stone disease.

Life cycle adapted upstream open reading frames (uORFs) in Trypanosoma congolense: A post-transcriptional approach to accurate gene regulation.

The presented work explores the regulatory influence of upstream open reading frames (uORFs) on gene expression in Trypanosoma congolense. More than 31,000 uORFs in total were identified and characterized here. We found evidence for the uORFs’ appearance in the transcriptome to be correlated with proteomic expression data, clearly indicating their repressive potential in T. congolense, which has to rely on post-transcriptional gene expression regulation due to its unique genomic organization. Our data show that uORF’s translation repressive potential does not only correlate with elemental sequence features such as length, position and quantity, but involves more subtle components, in particular the codon and amino acid profiles. This corresponds with the popular mechanistic model of a ribosome shedding initiation factors during the translation of a uORF, which can prevent reinitiation at the downstream start codon of the actual protein-coding sequence, due to the former extensive consumption of crucial translation components. We suggest that uORFs with uncommon codon and amino acid usage can slow down the translation elongation process in T. congolense, systematically deplete the limited factors, and restrict downstream reinitiation, setting up a bottleneck for subsequent translation of the protein-coding sequence. Additionally we conclude that uORFs dynamically influence the T. congolense life cycle. We found evidence that transition to epimastigote form could be supported by gain of uORFs due to alternative trans-splicing, which down-regulate housekeeping genes’ expression and render the trypanosome in a metabolically reduced state of endurance.

New approaches based on comparative proteomics for the assessment of food quality

During the last decade, the use of integrated ‘omics’ including genomics, transcriptomics, metabolomics and proteomics has provided a better knowledge of food systems. In fact, technical improvements achieved in mass spectrometry instruments together with the last developments of bioinformatics tools and hardware have allowed proteomics to be the method of choice for food control through the identification of quality biomarkers and the study of the proteome expression data. In this context, modern comparative proteomics is the basis for establishing quantitative differences in the expressed proteome of a certain sample by comparison with a reference through the relative and absolute quantification of proteins and peptides.

Elevated CO2 increases R gene-dependent resistance of Medicago truncatula against the pea aphid by up-regulating a heat shock gene.

Resistance against pathogens and herbivorous insects in many plant results from the expression of resistance (R) genes. Few reports, however, have considered the effects of elevated CO2 on R gene-based resistance in plants. The current study determined the responses of two near isogenic Medicago truncatula genotypes (Jester has an R gene and A17 does not) to the pea aphid and elevated CO2 in open-top chambers in the field. Aphid abundance, mean relative growth rate and feeding efficiency were increased by elevated CO2 on A17 plants but were reduced on Jester plants. According to proteomic and gene expression data, elevated CO2 enhanced pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) but decreased the effector-triggered immunity (ETI) in aphid-infested A17 plants. For aphid-infested Jester plants, by contrast, elevated CO2 enhanced the ETI-related heat shock protein (HSP) 90 and its co-chaperones, the jasmonic acid (JA) signaling pathway, and ubiquitin-mediated proteolysis. In a loss-of-function experiment, silencing of the HSP90 gene in Jester plants impaired the JA signaling pathway and ubiquitin-mediated proteolysis against the aphid under ambient CO2 , and negated the increased resistance against the aphid under elevated CO2 . Our results suggest that increases in expression of HSP90 are responsible for the enhanced resistance against the aphid under elevated CO2 .

Abstract B1-26: NetFill: A network-based method to identify a parsimonious set of mediators to link mRNA and protein signatures using existing knowledge

Abstract Introduction: Despite our general understanding of the genetic information flow from DNA to RNA to protein, the integrated analysis of multi-level omics data in a biologically meaningful way remains a challenging task due to the complex and dynamic nature of disease associated changes in molecular regulatory mechanisms. A case in point is the often observed phenomenon that key signatures associated with the same phenotypic difference do not share much in common between the mRNA and proteomic data even when they are measured on a common set of samples. Methods: We developed a computational method, NetFill, that integrates prior knowledge into the analysis of expression data to identify a parsimonious set of molecular mediators to connect the mRNA and protein signatures with plausible pathways/networks. NetFill formulates the problem mathematically as a flow-based linear programming under constraints to identify mediator/proteins in the protein-protein interactome network with the minimal cost cumulated from selected nodes and selected edges to link the mRNA and protein signatures. Data dependent node score and edge score were computed from the mRNA and proteomic expression data using Bayes' rule. By assuming conditional independence between different types of evidence, the node score was derived from the p values of t-tests between two conditions. Edge score was derived from the p-value of correlation between two proteins and edge confidence score from static PPI network. In the current implementation of NetFill, nine protein-protein interaction databases (BIND, BioGRID, DIP, HPRD, IntAct, MINT, MIPS, PDZBase, and Ractome) and three pathway databases (KEGG, Biocarta, and NCI) were combined to generate a large protein-protein interactome network with 101,398 interactions. To assess the confidence score of selected interactions, the network search algorithm was applied on 100 bootstrap with replacement samples of the mRNA and protein data. The final output of NetFill was a high-confidence network in which all edges had occurred in > 50% of the bootstrap sampling generated networks. Results: Resulted from separate consensus clustering analyses, an mRNA signature of 213 genes and a proteomic signature of 73 proteins for a common subtype/cluster of ovarian high-grade serous carcinoma (HGSC) samples had only 40 overlapping gene/proteins. NetFill identified a parsimonious network with 142 additional mediators to link the mRNA and proteomic signatures. Functional annotation revealed that Wnt signaling pathway (p = 0.01) and MAPK signaling pathway (p = 0.03) were significantly enriched in the identified network, indicating that these pathways could be potentially involved in this subtype of ovarian HGSC. Conclusion: The NetFill method and reported example demonstrated that existing knowledge of plausible interactions among genes and proteins could be used to help to functionally integrate mRNA and protein data in multi-level omics characterization of samples. Citation Format: Li Chen, Bai Zhang, Hui Zhang, Yue Wang, Daniel W. Chan, Zhen Zhang. NetFill: A network-based method to identify a parsimonious set of mediators to link mRNA and protein signatures using existing knowledge. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-26.

Transcription Factor 7 Limits Regulatory T Cell Generation in the Thymus.

Regulatory T cells (Tregs) differentiate in the thymus, but the mechanisms that control this process are not fully understood. We generated a comprehensive quantitative and differential proteome of murine Tregs and conventional T cells. We identified 5225 proteins, 164 of which were differentially expressed in Tregs. Together with the comparative analysis of proteome and gene expression data, we identified TCF7 as a promising candidate. Genetic elimination of transcription factor 7 (TCF7) led to increased fractions of Tregs in the thymus. Reduced levels of TCF7, found in the heterozygote, resulted in a greater potential for Treg precursors to differentiate into the Treg lineage. In contrast, activation of TCF7 through β-catenin had the opposite effect. TCF7 levels influenced the required TCR signaling strength of Treg precursors, and TCF7 deficiency broadened the repertoire and allowed lower TCR affinities to be recruited into the Treg lineage. FOXP3 was able to repress TCF7 protein expression. In summary, we propose a regulatory role for TCF7 in limiting access to the Treg lineage.

Comparative proteome analysis across non-small cell lung cancer cell lines

Non-small cell lung cancer (NSCLC) cell lines are widely used model systems to study molecular aspects of lung cancer. Comparative and in-depth proteome expression data across many NSCLC cell lines has not been generated yet, but would be of utility for the investigation of candidate targets and markers in oncogenesis. We employed a SILAC reference approach to perform replicate proteome quantifications across 23 distinct NSCLC cell lines. On average, close to 4000 distinct proteins were identified and quantified per cell line. These included many known targets and diagnostic markers, indicating that our proteome expression data represents a useful resource for NSCLC pre-clinical research. To assess proteome diversity within the NSCLC cell line panel, we performed hierarchical clustering and principal component analysis of proteome expression data. Our results indicate that general proteome diversity among NSCLC cell lines supersedes potential effects common to K-Ras or epidermal growth factor receptor (EGFR) oncoprotein expression. However, we observed partial segregation of EGFR or KRAS mutant cell lines for certain principal components, which reflected biological differences according to gene ontology enrichment analyses. Moreover, statistical analysis revealed several proteins that were significantly overexpressed in KRAS or EGFR mutant cell lines.

Proteomic LC–MS analysis of Arabidopsis cytosolic ribosomes: Identification of ribosomal protein paralogs and re-annotation of the ribosomal protein genes

Arabidopsis thaliana cytosolic ribosomes are large complexes containing eighty-one distinct ribosomal proteins (r-proteins), four ribosomal RNAs (rRNA) and a plethora of associated (non-ribosomal) proteins. In plants, r-proteins of cytosolic ribosomes are each encoded by two to seven different expressed and similar genes, forming an r-protein family.Distinctions in the r-protein coding sequences of gene family members are a source of variation between ribosomes. We performed proteomic investigation of actively translating cytosolic ribosomes purified using both immunopurification and a classic sucrose cushion centrifugation-based protocol from plants of different developmental stages. Both 1D and 2D LC–MSE with data-independent acquisition as well as conventional data-dependent MS/MS procedures were applied. This approach provided detailed identification of 165 r-protein paralogs with high coverage based on proteotypic peptides. The detected r-proteins were the products of the majority (68%) of the 242 cytosolic r-protein genes encoded by the genome. A total of 70 distinct r-proteins were identified. Based on these results and information from DNA microarray and ribosome footprint profiling studies a re-annotation of Arabidopsis r-proteins and genes is proposed. This compendium of the cytosolic r-protein proteome will serve as a template for future investigations on the dynamic structure and function of plant ribosomes. Biological significanceTranslation is one of the most energy demanding processes in a living cell and is therefore carefully regulated. Translational activity is tightly linked to growth control and growth regulating mechanism. Recently established translational profiling technologies, including the profiling of mRNAs associated with polysomes and the mapping of ribosome footprints on mRNAs, have revealed that the expression of gene expression is often fine-tuned by differential translation of gene transcripts. The eukaryotic ribosome, the hub of these important processes, consists of close to eighty different proteins (depending on species) and four large RNAs assembled into two highly conserved subunits.In plants and to lesser extent in yeast, the r-proteins are encoded by more than one actively transcribed gene. As r-protein gene paralogs frequently do not encode identical proteins and are regulated by growth conditions and development, in vivo ribosomes are heterogeneous in their protein content. The regulatory and physiological importance of this heterogeneity is unknown. Here, an improved annotation of the more than two hundred r-protein genes of Arabidopsis is presented that combines proteomic and advanced mRNA expression data. This proteomic investigation and re-annotation of Arabidopsis ribosomes establish a base for future investigations of translational control in plants.

Proteomic analysis of salt-stress responsive proteins in roots of tomato (Lycopersicon esculentum L.) plants towards silicon efficiency

Salt stress is a rising threat to crop productivity, among several notable consumed crops tomato is often under threat due to salt stress. In this study, the role of Si in restraining salinity stress responses in root proteome and genes involved in stress tolerance has been studied. Fifteen days old tomato (Lycopersicon esculentum L.) plants grown hydroponically with or without NaCl were fed with 2.5 mM Si in the form of potassium silicate [K2SiO3]. The response to a combined effect of NaCl and Si were studied 5 days after treatment. Proteomic analysis indicated that 40 proteins were differentially expressed under Si and/or salt stress treatments. Twenty-four of them were up-regulated by Si supplements (50 mM +NaCl/+Si) and down-regulated in salt-stressed roots (50 mM +NaCl/−Si), and these proteins were mostly associated with stress responses, plant hormones and transcriptional regulations. The rest of them belong to other secondary metabolites. Moreover, 17 differentially expressed proteins (25 mM +NaCl/+Si), up-regulated in the Si treatments, were mostly related to stress responses, plant hormones and cellular biosynthesis, and the rest of the proteins were related to transcriptional regulation, RNA binding and other secondary metabolisms. In addition, 17 protein spots were observed absent in salinity-stressed roots (25/or 50 mM +NaCl/−Si). Moreover, the important genes associated with salt stress responses (leDREB-1, leDREB-2 and leDREB-3), antioxidants (leAPX, leSOD and leCAT genes) and Si transport (leLsi-1, leLsi-2 and leLsi-3) were analyzed by the real-time polymerase chain reaction. The physiological data such as thiobarbituric acid reactive substances, superoxide dismutase as an oxidative stress marker and concentration of Si all correlated well with proteomic and gene expression data. The observed responses to Si supply in salt stressed plants indicate that the Si has a substantial role in alleviating the salinity stress responses by improving the root proteome and activating important genes responsible for stress tolerance.

Meta-analysis of expression of hepatic organic anion-transporting polypeptide (OATP) transporters in cellular systems relative to human liver tissue.

Organic anion-transporting polypeptide (OATP)1B1, OATP1B3, and OATP2B1 transporters play an important role in hepatic drug disposition. Recently, an increasing number of studies have reported proteomic expression data for OATP transporters. However, systematic analysis and understanding of the actual differences in OATP expression between liver tissue and commonly used cellular systems is lacking. In the current study, meta-analysis was performed to assess the protein expression of OATP transporters reported in hepatocytes relative to liver tissue and to identify any potential correlations in transporter expression levels in the same individual. OATP1B1 was identified as the most abundant uptake transporter at 5.9 ± 8.3, 5.8 ± 3.3, and 4.2 ± 1.7 fmol/μg protein in liver tissue, sandwich-cultured human hepatocytes (SCHH), and cryopreserved suspended hepatocytes, respectively. The rank order in average expression in liver tissue and cellular systems was OATP1B1 > OATP1B3 ≈ OATP2B1. Abundance levels of the OATP transporters investigated were not significantly different between liver and cellular systems, with the exception of OATP2B1 expression in SCHH relative to liver tissue. Analysis of OATP1B1, OATP1B3, and OATP2B1 liver expression data in the same individuals (n = 86) identified weak (OATP1B1-OATP2B1) to moderately (OATP1B3-OATP2B1) significant correlations. A significant weak correlation was noted between OATP1B1 abundance and age of human donors, whereas expression of the OATPs investigated was independent of sex. Implications of the current analysis on the in vitro-in vivo extrapolation of transporter-mediated drug disposition using physiologically based pharmacokinetic models are discussed.

Adaption of the global test idea to proteomics data with missing values

Global test procedures are frequently used in gene expression analysis to study the relationship between a functional subset of RNA transcripts and an experimental group factor. However, these procedures have been rarely used for the analysis of high-throughput data from other sources, such as proteome expression data. The main difficulties in transferring global test procedures from genomics to proteomics data are the more complicated way of obtaining functional annotations and the handling of missing values in some types of proteomics data. We propose a simple mixed linear model in combination with a permutation procedure and missing values imputation to conduct global tests in proteomics experiments. This new approach is motivated by protein expression data obtained by means of 2-D gel electrophoresis within a mouse experiment of our current research. A simulation study yielded that power and testing level of the mixed model alone can be affected by missing values in the dataset. Imputation of missing values was able to correct for a bias in some simulation settings. Our new approach provides the possibility to rank Gene Ontology (GO) terms associated with protein sets. It is also helpful in the case in which a specific protein is represented by multiple spots on a 2-D gel by considering these spots also as a protein set. Analysis of our data points at correlations between the deficiency of the protein 'calreticulin' and protein sets related to biological processes in the heart muscle. Our proposed approach is included in the R-package 'RepeatedHighDim', which already contains a global test procedure for gene expression data. The package can be retrieved from http://cran.r-project.org/. klaus.jung@ams.med.uni-goettingen.de.

Two-layer modular analysis of gene and protein networks in breast cancer.

BackgroundGenomic, proteomic and high-throughput gene expression data, when integrated, can be used to map the interaction networks between genes and proteins. Different approaches have been used to analyze these networks, especially in cancer, where mutations in biologically related genes that encode mutually interacting proteins are believed to be involved. This system of integrated networks as a whole exhibits emergent biological properties that are not obvious at the individual network level. We analyze the system in terms of modules, namely a set of densely interconnected nodes that can be further divided into submodules that are expected to participate in multiple biological activities in coordinated manner.ResultsIn the present work we construct two layers of the breast cancer network: the gene layer, where the correlation network of breast cancer genes is analyzed to identify gene modules, and the protein layer, where each gene module is extended to map out the network of expressed proteins and their interactions in order to identify submodules. Each module and its associated submodules are analyzed to test the robustness of their topological distribution. The constituent biological phenomena are explored through the use of the Gene Ontology. We thus construct a “network of networks”, and demonstrate that both the gene and protein interaction networks are modular in nature. By focusing on the ontological classification, we are able to determine the entire GO profiles that are distributed at different levels of hierarchy. Within each submodule most of the proteins are biologically correlated, and participate in groups of distinct biological activities.ConclusionsThe present approach is an effective method for discovering coherent gene modules and protein submodules. We show that this also provides a means of determining biological pathways (both novel and as well those that have been reported previously) that are related, in the present instance, to breast cancer. Similar strategies are likely to be useful in the analysis of other diseases as well.

ProteoMirExpress: Inferring MicroRNA and Protein-centered Regulatory Networks from High-throughput Proteomic and mRNA Expression Data

MicroRNAs (miRNAs) regulate gene expression through translational repression and RNA degradation. Recently developed high-throughput proteomic methods measure gene expression changes at protein level and therefore can reveal the direct effects of miRNAs' translational repression. Here, we present a web server, ProteoMirExpress, that integrates proteomic and mRNA expression data together to infer miRNA-centered regulatory networks. With both types of high-throughput data from the users, ProteoMirExpress is able to discover not only miRNA targets that have decreased mRNA, but also subgroups of targets with suppressed proteins whose mRNAs are not significantly changed or with decreased mRNA whose proteins are not significantly changed, which are usually ignored by most current methods. Furthermore, both direct and indirect targets of miRNAs can be detected. Therefore, ProteoMirExpress provides more comprehensive miRNA-centered regulatory networks. We used several published data to assess the quality of our inferred networks and prove the value of our server. ProteoMirExpress is available online, with free access to academic users.

Functional genomics suggest neurogenesis in the adult human olfactory bulb

The human olfactory bulb displays high morphologic dynamics changing its volume with olfactory function, which has been explained by active neurogenetic processes. Discussion continues whether the human olfactory bulb hosts a continuous turnover of neurons. We analyzed the transcriptome via RNA quantification of adult human olfactory bulbs and intersected the set of expressed transcriptomic genes with independently available proteomic expression data. To obtain a functional genomic perspective, this intersection was analyzed for higher-level organization of gene products into biological pathways established in the gene ontology database. We report that a fifth of genes expressed in adult human olfactory bulbs serve functions of nervous system or neuron development, half of them functionally converging to axonogenesis but no other non-neurogenetic biological processes. Other genes were expectedly involved in signal transmission and response to chemical stimuli. This provides a novel, functional genomics perspective supporting the existence of neurogenesis in the adult human olfactory bulb.

Use of Human In Vitro Skin Models for Accurate and Ethical Risk Assessment: Metabolic Considerations

Several human skin models employing primary cells and immortalized cell lines used as monocultures or combined to produce reconstituted 3D skin constructs have been developed. Furthermore, these models have been included in European genotoxicity and sensitization/irritation assay validation projects. In order to help interpret data, Cosmetics Europe (formerly COLIPA) facilitated research projects that measured a variety of defined phase I and II enzyme activities and created a complete proteomic profile of xenobiotic metabolizing enzymes (XMEs) in native human skin and compared them with data obtained from a number of in vitro models of human skin. Here, we have summarized our findings on the current knowledge of the metabolic capacity of native human skin and in vitro models and made an overall assessment of the metabolic capacity from gene expression, proteomic expression, and substrate metabolism data. The known low expression and function of phase I enzymes in native whole skin were reflected in the in vitro models. Some XMEs in whole skin were not detected in in vitro models and vice versa, and some major hepatic XMEs such as cytochrome P450-monooxygenases were absent or measured only at very low levels in the skin. Conversely, despite varying mRNA and protein levels of phase II enzymes, functional activity of glutathione S-transferases, N-acetyltransferase 1, and UDP-glucuronosyltransferases were all readily measurable in whole skin and in vitro skin models at activity levels similar to those measured in the liver. These projects have enabled a better understanding of the contribution of XMEs to toxicity endpoints.

PanelComposer: A Web-Based Panel Construction Tool for Multivariate Analysis of Disease Biomarker Candidates

Measuring and evaluating diagnostic efficiency is important in biomarker discovery and validation. The receiver operating characteristic (ROC) curve is a graphical plot for assessing the performance of a classifier or predictor that can be used to test the sensitivity and specificity of diagnostic biomarkers. In this study, we describe PanelComposer, a Web-based software tool that uses statistical results from proteomic expression data and validates biomarker candidates based on ROC curves and the area under the ROC curve (AUC) values using a logistic regression model and provides an ordered list that includes ROC graphs and AUC values for proteins (individually or in combination). This tool allows users to easily compare and assess the effectiveness and diagnostic efficiency of single or multiprotein biomarker candidates. PanelComposer is available publicly at http://panelcomposer.proteomix.org/ and is compatible with major Web browsers.

2D electrophoresis-based expression proteomics: a microbiologist’s perspective

Quantitative proteomics based on 2D electrophoresis (2-DE) coupled with peptide mass fingerprinting is still one of the most widely used quantitative proteomics approaches in microbiology research. Our view on the exploitation of this global expression analysis technique and its contribution and potential to push forward the field of molecular microbial physiology towards a molecular systems microbiology perspective is discussed in this article. The advances registered in 2-DE-based quantitative proteomic analysis leading to increased protein resolution, sensitivity and accuracy, and the promising use of 2-DE to gain insights into post-translational modifications at a proteome-wide level (considering all the proteins/protein forms expressed by the genome) are focused on. Given the progress made in this field, it is foreseen that the 2-DE-based approach to quantitative proteomics will continue to be a fundamental tool for microbiologists working at a genome-wide scale. Guidelines are also provided for the exploitation of expression proteomics data, based on useful computational tools, and for the integration of these data with other genome-wide expression information. The advantages and limitations of a complete 2-DE-based expression proteomics analysis, envisaging the quantification of the global changes occurring in the proteome of a given cell depending on environmental or genetic manipulations, are discussed from the microbiologist’s perspective. Particular focus is given to the emerging field of toxicoproteomics, a new systems toxicity approach that offers a powerful tool to directly monitor the earliest stages of the toxicological response by identifying critical proteins and pathways that are affected by, and respond to, a chemical stress. The experimental design and the bioinformatics analysis of data used in our laboratory to gain mechanistic insights through expression proteomics into the responses of the eukaryotic model Saccharomyces cerevisiae or of Pseudomonas strains to environmental toxicants are presented as case studies.

Comparison of transcriptional and translational changes caused by long-term menadione exposure in Aspergillus nidulans

Under long-term oxidative stress caused by menadione sodium bisulfite, genome-wide transcriptional and proteome-wide translational changes were compared in Aspergillus nidulans vegetative cells. The comparison of proteomic and DNA microarray expression data demonstrated that global gene expression changes recorded with either flip-flop or dendrimer cDNA labeling techniques supported proteome changes moderately with 40% and 34% coincidence coefficients, respectively. Enzyme levels in the glycolytic pathway were alternating, which was a direct consequence of fluctuating gene expression patterns. Surprisingly, enzymes in the vitamin B2 and B6 biosynthetic pathways were repressed concomitantly with the repression of some protein folding chaperones and nuclear transport elements. Under long-term oxidative stress, the peroxide-detoxifying peroxiredoxins and cytochrome c peroxidase were replaced by thioredoxin reductase, a nitroreductase and a flavohemoprotein, and protein degradation became predominant to eliminate damaged proteins.

Signaling pathway networks mined from human pituitary adenoma proteomics data

BackgroundWe obtained a series of pituitary adenoma proteomic expression data, including protein-mapping data (111 proteins), comparative proteomic data (56 differentially expressed proteins), and nitroproteomic data (17 nitroproteins). There is a pressing need to clarify the significant signaling pathway networks that derive from those proteins in order to clarify and to better understand the molecular basis of pituitary adenoma pathogenesis and to discover biomarkers. Here, we describe the significant signaling pathway networks that were mined from human pituitary adenoma proteomic data with the Ingenuity pathway analysis system.MethodsThe Ingenuity pathway analysis system was used to analyze signal pathway networks and canonical pathways from protein-mapping data, comparative proteomic data, adenoma nitroproteomic data, and control nitroproteomic data. A Fisher's exact test was used to test the statistical significance with a significance level of 0.05. Statistical significant results were rationalized within the pituitary adenoma biological system with literature-based bioinformatics analyses.ResultsFor the protein-mapping data, the top pathway networks were related to cancer, cell death, and lipid metabolism; the top canonical toxicity pathways included acute-phase response, oxidative-stress response, oxidative stress, and cell-cycle G2/M transition regulation. For the comparative proteomic data, top pathway networks were related to cancer, endocrine system development and function, and lipid metabolism; the top canonical toxicity pathways included mitochondrial dysfunction, oxidative phosphorylation, oxidative-stress response, and ERK/MAPK signaling. The nitroproteomic data from a pituitary adenoma were related to cancer, cell death, lipid metabolism, and reproductive system disease, and the top canonical toxicity pathways mainly related to p38 MAPK signaling and cell-cycle G2/M transition regulation. Nitroproteins from a pituitary control related to gene expression and cellular development, and no canonical toxicity pathways were identified.ConclusionsThis pathway network analysis demonstrated that mitochondrial dysfunction, oxidative stress, cell-cycle dysregulation, and the MAPK-signaling abnormality are significantly associated with a pituitary adenoma. These pathway-network data provide new insights into the molecular mechanisms of human pituitary adenoma pathogenesis, and new clues for an in-depth investigation of pituitary adenoma and biomarker discovery.

Cancer proteome-expression database: Genome Medicine Database of Japan Proteomics

The use of proteome-expression databases may facilitate cancer research. Proteome-expression data derived from a sufficient number of clinical cases may help establish the molecular background of malignant tumors. Recently, we published a proteome expression database, the Genome Medicine Database of Japan (GeMDBJ) Proteomics. The GeMDBJ Proteomics includes the proteome data of surgically resected tissues and tissue-cultured cells of various malignancies, as well as the corresponding biological and clinicopathological data. The proteome data in GeMDBJ Proteomics include expression data produced using 2D PAGE with the immobilized pH gradient gel and DIGE technology, and the protein identification data by liquid chromatography tandem mass spectrometry. GeMDBJ Proteomics can be freely accessed online without registration.